Method and system for adding processes to print production workflows utilizing asset metadata and automated reasoning

ABSTRACT

A method and system for adding processes to print production workflows utilizing asset metadata and automated reasoning system is disclosed. The asset metadata can be extracted from the resources associated with a print product description such as PDF files and image and artwork files. The asset metadata can be processed through an automated reasoning system in order to infer additional metadata. The inferred metadata can be utilized to add and parameterize a process node in order to build a more effective and useful process network. The reasoning system can be a rule based reasoning system and/or ontology based reasoning system.

TECHNICAL FIELD

Embodiments are generally related to data processing methods andsystems. Embodiments are related to an intent to process conversionsystem and also relate to print process descriptions. Embodiments areadditionally related to methods and systems for adding processes toprint production workflows using asset metadata and automated reasoning.

BACKGROUND OF THE INVENTION

JDF (Job Definition Format) is an open, extensible, XML-based printworkflow specification framework. It ties together authoring,production, management, manufacturing, delivery, and MIS (ManagementInformation System) control. The intent to process conversion translatesa JDF job definition of the product intent into a JDF processspecification for specifying the manufacturing of the print product. Theproduct description captures the customer's product intent6t bydescribing the desired print product. Product description formats, suchas JDF product intent nodes, are intended to formalize the descriptionof a print product to make translation to a workflow easier.

The JDF product intent defines the product description of the finalproduct to be produced by the production shop. The product descriptionconsists of intent parameters of product characteristics for examplebinding, color models, finished size and references to artwork contentsuch as PDLs and supporting data files such as images, fonts, profiles,etc. The JDF intent to process conversion system transforms the intentdescription into a JDF process network of process nodes, and specifiesdynamic process parameters of each process node for execution by aworkflow system. The process networks in combination with static processparameters set on specific workflow applications in the workflow systemresult in a fully populated workflow specification for the print shop.

Since such constructs are not typically meant to be human-readable,their use can be complicated and prone to error. Prior art methods foradding processes to print production workflows are therefore based onmanual or improvised approaches, which cannot reliably and accuratelyprovide the most appropriate translation into a workflow and which,therefore, result in inefficient and time consuming processes. There isa need to provide a methodology for adding processes into anautomatically generated print shop process network which makestranslation of the product description to workflow easier.

Based on the foregoing it is believed that a need exists for an improvedmethod for adding processes to print production workflows utilizing anasset metadata and automated reasoning system. Additionally, a needexists for providing a methodology, which enables print productdescriptions to be effectively and rapidly transformed into a desiredend product.

BRIEF SUMMARY

The following summary is provided to facilitate an understanding of someof the innovative features unique to the embodiments disclosed and isnot intended to be a full description. A full appreciation of thevarious aspects of the embodiments can be gained by taking the entirespecification, claims, drawings, and abstract as a whole.

It is, therefore, one aspect of the present invention to provide for animproved data processing method and system.

It is another aspect of the present invention to provide for improvedintent to process conversion system.

It is a further aspect of the present invention to provide for animproved method and system for automatically adding new processes toprint production workflows.

The aforementioned aspects and other objectives and advantages can nowbe achieved as described herein. A method and system for addingprocesses to print production workflows utilizing asset metadata andautomated reasoning system is disclosed. The asset metadata can beextracted from the resources associated with a print product descriptionsuch as PDF files and image and artwork files. The asset metadata can beprocessed through an automated reasoning system in order to inferadditional metadata and asserted into facts as input into thetranslation of product intent to a workflow. The inferred metadata canbe utilized to add and parameterize a process node in order to buildmore effective and useful process network. The reasoning system can be arule based reasoning system and/or ontology based reasoning system.

The reasoning system is assumed to be rules-based, but other reasoningsystems, such as a knowledge base intent system utilizing semantic webtechnology, and specifically automated reasoning are equally applicable.The system can be provided with product descriptions, for example, a setof JDF product intent files that all represent the same product type.The product description contains references to artwork resources such asPDLs (Page Description Language), images, font files, variable data,etc. The PDLs themselves may contain images or other resourcereferences, which contain asset metadata. The metadata can be extractedfrom the resources and asserted into facts. In the rules-based reasoningsystem, the metadata characteristics are related to the pre-conditionsof the various classification rules. In the semantic web based reasoningsystem, the metadata characteristics are defined in an ontology ofmetadata, which specifies logic-based property restrictions on themetadata concepts.

In accordance with additional features of the present invention, modulescan be adapted for automatically adding process nodes to printproduction workflows to infer knowledge from asset metadata tags and usesaid knowledge during process network generation via a module adapted toprovide a print product description, a module adapted to extract assetmetadata from a plurality of resources associated with said printproduct description and a module adapted to utilize said inferredmetadata to add and parameterize a process node including said inferredmetadata to a process network. A processor associated with the systemcan be adapted to process said asset metadata through said automatedreasoning system to infer predefined characteristics from said assetmetadata to form inferred metadata.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures, in which like reference numerals refer toidentical or functionally-similar elements throughout the separate viewsand which are incorporated in and form a part of the specification,further illustrate the embodiments and, together with the detaileddescription, serve to explain the embodiments disclosed herein.

FIG. 1 illustrates a block diagram of a data-processing apparatus, whichcan be utilized for adding processes to print production workflows, inaccordance with a feature of the present invention;

FIG. 2 illustrates a block diagram of the overall workflow for addingprocesses to print production workflows, in accordance with anotherfeature of the present invention;

FIG. 3 illustrates an exemplary embodiment of a visualizedknowledge-base illustrating JDF product intent to inferred metadatautilizing a reasoning system, in accordance with another feature of thepresent invention;

FIG. 4 illustrates an exemplary embodiment of a visualizedknowledge-base interface illustrating generation of process nodes, inaccordance with another feature of the present invention; and

FIG. 5 illustrates a high level flow chart of operations illustratinglogical operational steps of a method for adding processes to printproduction workflows using asset metadata and automated reasoning, inaccordance with another feature of the present invention.

FIG. 6 illustrates an exemplary screen shot of an interface representingaspects of the present invention in a format that can be viewed by auser.

DETAILED DESCRIPTION

The particular values and configurations discussed in these non-limitingexamples can be varied and are cited merely to illustrate at least oneembodiment and are not intended to limit the scope thereof.

DEFINITIONS

Intent2Process: The automatic conversion of a formal print productdescription, such as JDF Intent, into a Process Network (workflow) thatcan be used to manufacture the desired product. Intent2Processtechnology is currently deployed in the FreeFlow product suite.

JDF: The Job Definition Format. A formal language that describes bothprint products and the processes required for creating print products.Specifically JDF describes print products and their manufacturingprocesses used in print shops.

JDF Process: A set of processing instructions sufficient to describe thedetails of a particular process step. Examples of JDF Process Nodes are:Digital Printing, Imposition, Stitching, (a set of processinginstructions).

JDF Product Intent (JDF Intent): A formal description of a print productsuch as a Book, Business Card or Brochure. (more generally, a ProductDescription).

Product Description: A print product described using a formalunambiguous vocabulary. An example of a product description vocabularyis the JDF Product Node.

Processing Instructions: Instructions sufficient to execute a particularprocess. An example of processing instructions is the Process Nodeconstruct in JDF.

Process Network: A set of processing instructions that when executed inthe order specified by the process network results in a print product. Aprocess network is synonymous with a workflow.

The embodiments described herein can be implemented in the context of ahost operating system and one or more modules. Such modules mayconstitute hardware modules, such as, for example, electronic componentsof a computer system. Such modules may also constitute software modules.In the computer programming arts, a software “module” can be typicallyimplemented as a collection of routines and data structures thatperforms particular tasks or implements a particular abstract data type.

Software modules generally can include instruction media storable withina memory location of an image processing apparatus and are typicallycomposed of two parts. First, a software module may list the constants,data types, variable, routines and the like that can be accessed byother modules or routines. Second, a software module can be configuredas an implementation, which can be private (i.e., accessible perhapsonly to the module), and that contains the source code that actuallyimplements the routines or subroutines upon which the module is based.The term “module” as utilized herein can therefore generally refer tosoftware modules or implementations thereof. Such modules can beutilized separately or together to form a program product that can beimplemented through signal-bearing media, including transmission mediaand/or recordable media. An example of such a module that can embodyfeatures of the present invention is module 111 depicted in FIG. 1.

It is important to note that, although the embodiments are described inthe context of a fully functional data-processing system (e.g., acomputer system), those skilled in the art will appreciate that themechanisms of the embodiments are capable of being distributed as aprogram product in a variety of forms, and that the present inventionapplies equally regardless of the particular type of signal-bearingmedia utilized to actually carry out the distribution. Examples ofsignal bearing media include, but are not limited to, recordable-typemedia such as media storage or CD ROMs and transmission-type media suchas analogue or digital communications links. The logical operation stepsdepicted in FIGS. 2-3 can, for example, be implemented in the context ofsuch a software module.

Referring to the drawings and in particular to FIG. 1, there is depicteda data-processing apparatus 100 which can be utilized for addingprocesses to print production workflows using asset metadata andautomated reasoning. Data-processing apparatus 100 represents one ofmany possible data-processing and/or computing devices, which can beutilized in accordance with the disclosed embodiments. It can beappreciated that data-processing apparatus 100 and its components arepresented for generally illustrative purposes only and do not constitutelimiting features of the disclosed embodiments.

As depicted in FIG. 1, a memory 105, a processor (CPU) 110, a Read-Onlymemory (ROM) 115, and a Random-Access Memory (RAM) 120 are generallyconnected to a system bus 125 of data-processing apparatus 100. Memory105 can be implemented as a ROM, RAM, a combination thereof, or simply ageneral memory unit. Module 111 includes software module in the form ofroutines and/or subroutines for carrying out features of the presentinvention and can be additionally stored within memory 105 and thenretrieved and processed via processor 110 to perform a particular task.A user input device 140, such as a keyboard, mouse, or another pointingdevice, can be connected to PCI (Peripheral Component Interconnect) bus145. Module 111 can be adapted for automatically adding process nodes toprint production workflows to infer knowledge from asset metadata tagsand use said knowledge during process network generation via a moduleadapted to provide a print product description, a module adapted toextract asset metadata from a plurality of resources associated withsaid print product description and a module adapted to utilize saidinferred metadata to add and parameterize a process node including saidinferred metadata to a process network. Processor 110 can be adapted toprocess said asset metadata through said automated reasoning system toinfer predefined characteristics from said asset metadata to forminferred metadata.

Data-process apparatus 100 can thus include CPU 110, ROM 115, RAM 120,and a rendering device 190 (e.g., printer, copier, scanner, etc.), whichare also coupled to a PCI (Peripheral Component Interconnect) local bus145 of data-processing apparatus 100 through PCI host-bridge 135. ThePCI Host Bridge 135 can provide a low latency path through whichprocessor 110 may directly access PCI devices mapped anywhere within busmemory and/or input/output (I/O) address spaces. PCI Host Bridge 135also can provide a high bandwidth path for allowing PCI devices todirectly access RAM 120.

A communications adapter 155, a small computer system interface (SCSI)150, a raster image processor (RIP) 180, and an expansion bus-bridge 170can also be attached to PCI local bus 145. The communications adapter155 can be utilized for connecting data-processing apparatus 100 to anetwork 165. SCSI 150 can be utilized to control high-speed SCSI diskdrive 160. An expansion bus-bridge 170, such as a PCI-to-ISA bus bridge,may be utilized for coupling ISA bus 175 to PCI local bus 145. Note thatPCI local bus 145 can further be connected to a monitor 130, whichfunctions as a display (e.g., a video monitor) for displaying data andinformation for a user and also for interactively displaying a graphicaluser interface (GUI) 185.

Note that the term “GUI” generally refers to a type of environment thatrepresents programs, files, options and so forth by means of graphicallydisplayed icons, menus, and dialog boxes on a computer monitor screen. Auser can interact with the GUI 185 to select and activate such optionsby pointing and clicking with a user input device such as, for example,a pointing device such as a mouse, and/or with a keyboard. A particularitem can function in the same manner to the user in all applicationsbecause the GUI 185 can provide standard software routines (e.g., module111) to handle these elements and reports the user's actions.

In this regard, a user actuates the appropriate keys on the userinterface 185 to adjust the parameters of a print job. A user can accessand operate the rendering device 190 using the user interface 185. Thereasoning system can be a software module such as, for example, themodule 111 of apparatus 100 depicted in FIG. 1. The reasoning system isassumed to be rules-based and ontology based reasoning system utilizingsemantic web technology. Considering the fact that a product descriptionmust always be transformed into a set of processing instructions inorder to actually create the described product, one of thepre-requisites for the automated conversion of intent to process is awell-defined product description. An example is JDF product intent. Thesystem starts with asset metadata and reasoning system for addingprocesses to print production workflows.

Referring to FIG. 2 block diagram 200 of the overall workflow for addingprocesses to print production workflow is illustrated, in accordancewith a feature of the present invention. The print product description210 can be in the form of job definition format (JDF) product nodes fordescribing products and processes used in print shops. The productdescription 210 consists of intent parameters of product characteristicsfor example binding, color models, finished size and references toartwork content such as PDLs and supporting data files such as images,fonts, profiles, etc. These characteristics contain metadata and can beextracted as raw metadata or asset metadata 220. The asset metadata 220can be categorized using an automated reasoning system 230. The assetmetadata 220 extracted from the product description 210 can be loadedinto the rules-engine 235 so as to assert the asset metadata 220 withinthe rules engine 235.

The rules engine 235 can be classified into rules based system 240 andontology based system 250. The reasoning of ontology-based system 250can be done using description logics or other higher-order logics. Therules engine 235 makes its classification based on the final state ofthe output component facts, which are modified by rules firing regardingthe input component facts. The inferred metadata 260 can be refined orextended to generate process nodes 270. The process networks are builtfrom process nodes 270 and populated with parameters based on theasserted facts from the product description 210. Similarly, the factsasserted from the asset metadata 220 can be used to add and parameterizeprocess nodes 270. The system may prompt the user for verification ofthe categorized asset metadata 220 characteristics and the addition ofprocess nodes 270 through a rules/process modification interface 280.

The process nodes 270 can be utilized for process network generation290. For example, the product description 210 may reference a PDF file.The PDF file may include an XMP-based tag that labels a JPEG component,for example such as a “golf tournament”, as asset metadata 220. Duringprocess node creation, a “photograph subject ontology” can be utilizedby the automated reasoning system 230 in order to classify the image as“outdoors” and a color management pre-press node can be added which usesan appropriate color profile for the image taken in outdoor conditions.

Referring to FIG. 3 an exemplary embodiment of a graphical view of aknowledge-base 300 illustrating JDF product intent to inferred metadatausing reasoning system is illustrated, in accordance with a feature ofthe present invention. The knowledge-base 300 includes asset metadata220 of JDF product intent files 310 (such as Image of Golf Tournament)and its asset metadata ontology 220 (such as using an ontology ofphotograph subjects containing knowledge about Outdoor Events). Theasset metadata 220 can be processed through an automated reasoningsystem 230 to form inferred metadata 260. The inferred metadata is thenused to determine the process in which to add in the translation of theproduct intent into a workflow (such as color management for outdoorconditions).

Referring to FIG. 4 an exemplary embodiment of a graphical viewknowledge-base 400 illustrating generation of process nodes isillustrated, in accordance with a feature of the present invention. Theknowledge-base 400 includes the JDF intent 310 and related process nodes270. For example, as illustrated in FIG. 3 the asset metadata 220indicates that an image is a golf tournament, and a golf tournament canbe classified as an outdoor activity utilizing the automated reasoningsystem 230, and therefore a color correction process can be includedthat does outdoor color correction.

Another example provides that the asset metadata 220 indicates that animage is a father, and a father can be classified as a person, then anautomated image enhancement process for red eye correction could beincluded that supports images of people's faces. Furthermore, the assetmetadata 220 indicate that an image has a resolution of 72 dpi, and anyresolution less than 150 dpi can be classified as ‘low resolution’. Apreflight and/or review process can be included that ensures poor imagesare caught early in the workflow. The interface 280 provides that theuser can accept or reject the process nodes 270 as suggested by theinference of the rule and/or knowledge-base for inclusion in theworkflow.

Referring to FIG. 5 a high level flow chart of operations illustratinglogical operational steps of a method 500 for adding processes to printproduction workflows using asset metadata and automated reasoning isillustrated, in accordance with a feature of the present invention. Areasoning system based on semantic classification can be provided, asshown at block 510. The semantic classification includes rules basedsystem and ontology based system. The print product description of aprint product can be provided, as illustrated at block 520. The rawmetadata stored in print product description can be extracted, as shownat block 530. The raw metadata can be processed through automatedreasoning system in order to infer additional metadata, as shown atblock 540. The inferred metadata characteristics can be used during thegeneration of process nodes, as indicated at block 550. As shown inblock 560, further reasoning of products descriptions can be undertakenconsistent with execution of the previous steps.

The JDF product node is a formal, rigorous, description of a productsuch as a book, a business card or a brochure. However, as alreadyindicated above, the print product description can be any formalunambiguous vocabulary which describes the print product. Similarly,asset metadata uses a vocabulary, for example, asset tags may use avocabulary based on an ontology created by using tools such as the XeroxGeneric Visual Categorizer or WordNet. Ontologies can also be developedfrom manual image tagging with keywords. Raw tags from the metadata canbe processed through automated classification to determine additionalinformation to be used by intent to process.

For example, if an image is tagged with the term ‘father’, the automatedreasoning system 230 can use one or more ontologies to infer additionaltags for the image such as ‘male’, ‘person’, and ‘adult’. Raw metadata220 and inferred metadata 260 as shown in FIG. 2 are then used to reasonabout image enhancement procedures that can be incorporated into theprocess nodes. For instance, this method can be configured to recognizethe tag ‘person’ and add a process for flesh-tone color correction onartwork with content including people. The asset metadata 220 caninclude the tag “father’, but semantic-based automated reasoning is usedto infer that tags such as ‘father’, ‘boy’, ‘uncle’, ‘scientist’, etc.are types of people, and therefore this method can add and parameterizeprocess nodes as desired. Referring to FIG. 6, an exemplary screen shot600 of an interface showing data fields including an asset classhierarchy 610, class annotations 620 and class description 630. In thescreen shot, a “Golf Tournament” is illustrated as the example of a typeof “Outdoor Event.”

Based on the foregoing it can be appreciated that a system can beprovided, through the use of one or more software modules as describedabove, which results in adding process description into print productionworkflows utilizing asset metadata and automated reasoning system. Themain advantage of this method is that it will automatically add aprocess node by utilizing ontology-based or rule-based systems to inferknowledge from asset metadata tags and then use such knowledge duringprocess network generation. This method allows the intent to processconversion to more effectively add a process and parameterize a process.Time is also saved because there is no need for administration supportto manually add process nodes based on asset content.

It will be appreciated that variations of the above-disclosed and otherfeatures and functions, or alternatives thereof, may be desirablycombined into many other different systems or applications. Also thatvarious presently unforeseen or unanticipated alternatives,modifications, variations or improvements therein may be subsequentlymade by those skilled in the art which are also intended to beencompassed by the following claims.

1. A method for automatically adding process nodes to print productionworkflows by inferring knowledge from asset metadata tags and using saidknowledge during process network generation, comprising: providing aprint product description; extracting asset metadata from a plurality ofresources associated with said print product description; processingsaid asset metadata through an automated reasoning system in order toinfer relevant information from said asset metadata to form inferredmetadata; and utilizing said inferred metadata to add and parameterize aprocess node to a process network.
 2. The method of claim 1 wherein saidautomated reasoning system further comprises rules-based reasoning. 3.The method of claim 2 wherein said rules-based reasoning furthercomprises a pre-condition of a plurality of classification rules.
 4. Themethod of claim 1 wherein said automated reasoning system furthercomprises ontology-based reasoning.
 5. The method of claim 4 whereinsaid ontology-based reasoning further comprises characteristics ontologyrepresented by description logics.
 6. The method of claim 1, whereinsaid automated reasoning further comprises a user interface and a moduleoperable to prompt a user to add said process node to said processnetwork.
 7. The method of claim 1 wherein said automated reasoningautomatically utilizes a predefined set of characteristics to add saidprocess node to said process network.
 8. The method of claim 1, whereinsaid product description comprises Job Definition Format (JDF) productintent nodes adapted for describing products and processes used in printshops.
 9. A method for automatically adding process nodes to printproduction workflows by inferring knowledge from asset metadata tags andusing said knowledge during process network generation, comprising:providing a print product description; extracting asset metadata from aplurality of resources associated with said print product description;processing said asset metadata through an automated reasoning system toinfer predefined characteristics from said asset metadata to forminferred metadata; and utilizing said inferred metadata to add andparameterize a process node to a process network.
 10. The method ofclaim 9 wherein said automated reasoning system further comprisesrules-based reasoning.
 11. The method of claim 10 wherein saidrules-based reasoning further comprises at least one pre-conditionextracted from a plurality of classification rules.
 12. The method ofclaim 9 wherein said automated reasoning system further comprisesontology-based reasoning.
 13. The method of claim 12 wherein saidontology-based reasoning further comprises characteristics ontologyrepresented by description logics.
 14. The method of claim 9, whereinsaid automated reasoning system further comprises a knowledge-base foradding said process node to said process network.
 15. The method ofclaim 9 wherein said automated reasoning system automatically utilizessaid predefined set of characteristics to add said process node to saidprocess network.
 16. The method of claim 9, wherein said automatedreasoning system further comprises a knowledge-base for adding assetmetadata definitions to a metadata ontology.
 17. An automated reasoningsystem adapted for automatically adding process nodes to printproduction workflows by inferring knowledge from asset metadata tags andusing said knowledge during process network generation, said automatedreasoning system comprising: a module adapted to provide a print productdescription; a module adapted to extract asset metadata from a pluralityof resources associated with said print product description; a processoradapted to process said asset metadata through said automated reasoningsystem to infer predefined characteristics from said asset metadata toform inferred metadata; and a module adapted to utilize said inferredmetadata to add and parameterize a process node including said inferredmetadata to a process network.
 18. The automated reasoning system ofclaim 17 further comprising a rules-based reasoning module.
 19. Theautomated reasoning system of claim 18 wherein said rules-basedreasoning module is adapted to extract at least one pre-condition from aplurality of classification rules.
 20. The automated reasoning system ofclaim 17 further comprising an ontology-based reasoning module adaptedto reference ontology characteristics via description logic.